Robust imitation learning using disturbance injections overcomes issues of limited variation in demonstrations. However, these methods assume demonstrations are optimal, and that policy stabilization can be learned via simple augmentations. In real-world scenarios, demonstrations are often of diverse-quality, and disturbance injection instead learns sub-optimal policies that fail to replicate desired behavior. To address this issue, this paper proposes a novel imitation learning framework that combines both policy robustification and optimal demonstration learning. Specifically, this combinatorial approach forces policy learning and disturbance injection optimization to focus on mainly learning from high task achievement demonstrations, while utilizing low achievement ones to decrease the number of samples needed. The effectiveness of the proposed method is verified through experiments using an excavation task in both simulations and a real robot, resulting in high-achieving policies that are more stable and robust to diverse-quality demonstrations. In addition, this method utilizes all of the weighted sub-optimal demonstrations without eliminating them, resulting in practical data efficiency benefits.

Scenarios requiring humans to choose from multiple seemingly optimal actions are commonplace, however standard imitation learning often fails to capture this behavior. Instead, an over-reliance on replicating expert actions induces inflexible and unstable policies, leading to poor generalizability in an application. To address the problem, this paper presents the first imitation learning framework that incorporates Bayesian variational inference for learning flexible nonparametric multi-action policies, while simultaneously robustifying the policies against sources of error, by introducing and optimizing disturbances to create a richer demonstration dataset. This combinatorial approach forces the policy to adapt to challenging situations, enabling stable multi-action policies to be learned efficiently. The effectiveness of our proposed method is evaluated through simulations and real-robot experiments for a table-sweep task using the UR3 6-DOF robotic arm. Results show that, through improved flexibility and robustness, the learning performance and control safety are better than comparison methods.

Learning from Demonstration depends on a robot learner generalising its learned model to unseen conditions. Generally, it is not feasible for a person to provide demonstrations that account for all possible variations in non-trivial tasks. While there are many learning methods that can handle interpolation of observed data effectively, extrapolation from observed data offers a much greater challenge. To address this problem, this paper proposes a modified Task-Parameterised Gaussian Mixture Regression method that considers the relevance of task parameters during trajectory generation, as determined by variance in the data. The benefits of the proposed method are first explored using a simulated reaching task data set. Here it is shown that the proposed method offers far-reaching, low-error extrapolation abilities that are different in nature to existing learning methods. Data collected from novice users for a real-world manipulation task is then considered, where it is shown that the proposed method is able to effectively reduce grasping performance errors by $\sim $40% and extrapolate to unseen grasp targets under real-world conditions. These results indicate the proposed method can benefit novice users by placing less reliance on the user to provide high quality demonstration data sets.

The low-cost manufacturing and maintenance of prostheses is of vital importance to their successful deployment in developing countries. Low-cost prosthesis actuation is generally achieved by combining pre-programmed control strategies, with surface-electromyographic measurements taken from the residual limb. In a standard setting, these signals are measured with disposable gel electrodes. However, this limit on electrode reuse requires that prosthesis users have a stable supply of electrodes. Alternatively, the textile electrodes sewn from conductive thread are studied in the context of hand gesture recognition to consider their future use with low-cost prostheses. In this paper, it is demonstrated that textile electrodes can be applied for gesture recognition. To do so, surface electromyography (sEMG) experiments are run in South Africa on three amputees where they were asked to perform gestures with their phantom limb (i.e., the missing limb segment). A gesture recognition method is implemented, and the classification accuracy with data recorded from textile electrodes is compared to that from gel electrodes. Further analysis examining the relationship between classifier performance and physiological parameters are performed. Results show that textile electrodes can be used to perform accurate gesture recognition, and are comparable to disposable gel electrodes. This demonstrates that low-cost sensory systems are not barrier to myoelectric control in developing countries.